CN110263264B - Method for acquiring social network key node - Google Patents

Method for acquiring social network key node Download PDF

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CN110263264B
CN110263264B CN201910580206.9A CN201910580206A CN110263264B CN 110263264 B CN110263264 B CN 110263264B CN 201910580206 A CN201910580206 A CN 201910580206A CN 110263264 B CN110263264 B CN 110263264B
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熊佳骏
刘琳岚
舒坚
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Nanchang Hangkong University
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Abstract

The invention discloses a method for acquiring a key node of a social network and application thereofThe social network diagram comprises the following steps: dividing the social network graph into a plurality of communities, and acquiring the node with the largest community importance index in each community to obtain a community core; taking any two community cores v which are mutually communicatediAnd vjObtaining viAnd vjThe isolation set of (1) capturing the isolation set with the least number of nodes to obtain the minimum isolation set S (t), recording the number of nodes in the S (t), counting all the forms of the S (t), and recording the total number of the forms betat(ii) a Calculating vtFor viAnd vjAnd calculating vtFor all with vtInfluence of community core as bridge point
Figure DDA0002112913730000011
Add up to get connected importance Ct(ii) a Repeat selection vtSeveral times and respectively calculate CtTaking out CtV corresponding to the maximum value oftNamely, the key nodes of the social network. The community network is divided into a plurality of communities, the social network graph can be analyzed more comprehensively by acquiring the influence of different community cores, and the obtained social network key nodes are more accurate.

Description

Method for acquiring social network key node
Technical Field
The invention relates to the technical field of social network key nodes, in particular to a method for acquiring social network key nodes.
Background
A social network (social network) refers to a relationship network formed by individuals or teams for communicating information. The social network belongs to a complex network and has the characteristics of small universe, non-standard property, community structure and the like.
Some nodes in the social network play a key role in the whole network and have high value for researchers. The key nodes are found out through the importance evaluation of the nodes, so that the reliability of the network can be improved or the maximum benefit can be obtained through the key protection of the nodes, and the nodes can be destroyed to break down the whole network to achieve the purpose of destruction. Therefore, it is of great significance to understand the network structure deeply and analyze the network characteristics from the perspective of the bridge node to find the key node. At present, various methods for taking key nodes of social networks are proposed at home and abroad, and the methods comprise a k-shell decomposition method and a concept of universal nodes.
The existing method for the social network key nodes is too coarse, is not suitable for large-scale networks such as social networks, has some limitations when evaluating the social network key nodes, and has poor accuracy in obtaining the social network structure.
Disclosure of Invention
The invention aims to provide a method for acquiring the key nodes of the social network, which can comprehensively analyze the social network.
A method for obtaining a key node of a social network is applied to a social network graph and comprises the following steps:
dividing the social network graph into a plurality of communities, and acquiring the node with the largest community importance index in each community to obtain a community core;
acquiring the nodes communicated with the two community cores to obtain bridge nodes, and selecting any bridge node vtTaking any two community cores v which are mutually communicatediAnd vjObtaining viAnd vjCapturing the isolated set with the least number of nodes and making the isolated set include vtObtaining a minimum isolation set S (t), recording the node number alpha in S (t)tCounting all forms of S (t), recording the total number of said forms betat；
According to the formula
Figure BDA0002112913710000021
Calculating vtFor viAnd vjInfluence of (2)
Figure BDA0002112913710000022
Where e is a natural base number, and calculating vtFor all with vtInfluence of the community core as a bridge point
Figure BDA0002112913710000023
Accumulate all
Figure BDA0002112913710000024
Get the communication importance Ct；
Repeat selection vtAnd respectively calculate CtTaking out CtV corresponding to the maximum value oftNamely, the key nodes of the social network.
The invention has the beneficial effects that: the community network is divided into a plurality of communities, the social network graph can be analyzed more comprehensively by acquiring the influence of different community cores, and the obtained social network key nodes are more accurate.
In addition, the method for acquiring the key nodes of the social network provided by the invention can also have the following additional technical characteristics:
further, the dividing the social network diagram into several communities previously includes:
calculating a community importance index for each node within the social networking graph.
Further, the step of calculating a community importance index for each node within the social networking graph comprises:
acquiring degree indexes of all nodes in the social network graph;
and repeatedly executing and deleting the node with the minimum degree index in the social network graph until all the nodes are deleted, and recording the times of repeatedly executing and deleting, namely the community importance index b.
Further, the step of dividing the social network diagram into a plurality of communities comprises:
taking any node v0, and respectively obtaining a first-order neighbor node v1, a second-order neighbor node v2 and a third-order neighbor node v3 of v0, wherein v0, v1, v2 and v3 are sequentially connected;
if v0 is larger than the community importance indexes of v1, v2 and v3, dividing v0, v1, v2 and v3 into a community;
and repeating the two steps to divide the whole social network graph into a plurality of communities.
Further, in the step of acquiring a node connected to two community cores, the node connected to two community cores is located between the two community cores.
Further, said obtaining viAnd vjThe step of isolating the set of cells of (1) comprises:
taking any node set, and judging v when the node set is deletediAnd vjWhether the connected state of (1) is disconnected;
if yes, the node set is viAnd vjThe isolated set of (1).
Further, all forms of S (t) are counted, and the total number of the forms beta is recordedtComprises the following steps:
performing a Capture includes vtMinimum isolated set S of1(t) adding S1(t) storing the data into a preset minimum isolation set database;
re-execution of the Capture includes vtMinimum isolated set S of2(t), judgment of S2(t) whether all the minimal set of isolation in the minimal set database are the same, if so, continuing to perform capture including vtMinimum isolated set S of3(t), otherwise S2(t) storing in said minimum insulation set database;
by analogy, repeatedly performing a number of captures includes vtMinimum isolated set S ofm(t), judgment of Sm(t) whether all the minimal set of isolation in the minimal set database are the same, if so, continuing to perform capture including vtMinimum isolated set S ofm+1(t), otherwise Sm(t) storing in said minimum insulation set database;
counting the minimum isolated set stored in the minimum isolated set database to obtain S (t) all forms of quantity betat。
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flowchart illustrating a method for obtaining key nodes of a social network according to a first embodiment of the present invention;
FIG. 2 is a diagram of a social networking scenario of the first embodiment of the present invention;
FIG. 3 is a diagram illustrating obtaining a community importance index according to a first embodiment of the present invention;
FIG. 4 is a diagram illustrating the community division according to the first embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. Several embodiments of the invention are presented in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Referring to fig. 1 and fig. 2, a first embodiment of the present invention provides a method for obtaining a key node of a social network, which is applied to a social network diagram, and includes the following steps.
S1, calculating community importance indexes of each node in the social network graph, dividing the social network graph into a plurality of communities, and obtaining the node with the maximum community importance index in each community to obtain a community core.
In this embodiment, an american aviation network data set is used as an experimental data sample to construct a social network diagram, and G ═ V, E is used to represent the topology structure of the social network.
It should be noted that, in a social network, nodes can be divided into three categories: common nodes, community cores, bridge nodes. The general nodes are sparsely connected with each other, and most of the nodes exist around the community core. The community core is used as a node which is most closely connected with other nodes in a community, and each community only has one community core. The bridge node is used as a node for communicating two communities and exists between two community cores, and the community cores can be connected with each other only through the bridge node.
Specifically, the step of calculating the community importance index of each node in the social network graph includes:
acquiring degree indexes of all nodes in the social network graph;
and repeatedly executing and deleting the node with the minimum degree index in the social network graph until all the nodes are deleted, and recording the times of repeatedly executing and deleting, namely the community importance index b.
Referring to fig. 3, for example, first, the nodes with degree 1 are deleted from the network, and the community importance index b of these nodes is 1. If the nodes with the degree less than or equal to 1 are deleted continuously, the nodes b are equal to 2. If the node with the degree less than or equal to 1 is deleted continuously (if the node with the degree less than or equal to 1 is deleted, b is 3). If there is no node with a degree of 1 or less, the node with a degree of 2 or less is found, and in fig. 3, these nodes b are 3.
In addition, the step of dividing the social networking graph into communities comprises:
taking any node v0, and respectively obtaining a first-order neighbor node v1, a second-order neighbor node v2 and a third-order neighbor node v3 of v0, wherein v0, v1, v2 and v3 are sequentially connected;
if v0 is larger than the community importance indexes of v1, v2 and v3, dividing v0, v1, v2 and v3 into a community;
and repeating the two steps to divide the whole social network graph into a plurality of communities.
It can be understood that, referring to fig. 4, taking node a as an example, it is found that there is no node in the periphery that is larger than the community importance index of node a, so that the nodes around node a that have the same community importance index and are adjacent are shrunk to a node set, and finally, the entire node set a and its nodes in the three-hop range are divided into a community.
S2, acquiring the nodes communicated with the two community cores to obtain bridge nodes, and selecting any bridge node vtTaking any two community cores v which are mutually communicatediAnd vjObtaining viAnd vjCapturing the isolated set with the least number of nodes and making the isolated set include vtObtaining the minimum isolation set S(t) recording the number of nodes within S (t) (. alpha.)tCounting all forms of S (t), recording the total number of said forms betat。
In the present embodiment, αtAnd judging by naked eyes. In other embodiments, the computer program may also be designed for retrieval.
Wherein vt is between vi and vj.
It should be noted that isolated sets (SS) are concepts in graph theory, and are generally used to solve the connectivity problem in the graph. Compared with the traditional key node judgment method, the isolation set method can better find the nodes which play a role in communication in the network, and has the advantages of low time complexity and the like in a large-scale network. The embodiment adopts the minimum isolation set, and can accurately embody the key nodes of the social network.
In particular, said obtaining viAnd vjThe step of isolating the set of cells of (1) comprises:
taking any node set, and judging v when the node set is deletediAnd vjWhether the connected state of (1) is disconnected;
if yes, the node set is viAnd vjThe isolated set of (1).
It will be understood that alpha istThe smaller the value of (A), the higher the node importance is; in addition, at αtUnder the same value of (b), betatThe larger the value of (c), the higher the importance of the node.
Specifically, all forms of S (t) are counted, and the total number of the forms beta is recordedtComprises the following steps:
performing a Capture includes vtMinimum isolated set S of1(t) adding S1(t) storing the data into a preset minimum isolation set database;
re-execution of the Capture includes vtMinimum isolated set S of2(t), judgment of S2(t) whether all the minimal set of isolation in the minimal set database are the same, if so, continuing to perform capture including vtMinimum isolated set S of3(t), otherwise S2(t) storing in said minimum insulation set database；
By analogy, repeatedly performing a number of captures includes vtMinimum isolated set S ofm(t), judgment of Sm(t) whether all the minimal set of isolation in the minimal set database are the same, if so, continuing to perform capture including vtMinimum isolated set S ofm+1(t), otherwise Sm(t) storing in said minimum insulation set database;
counting the minimum isolated set stored in the minimum isolated set database to obtain S (t) all forms of quantity betat。
In the present embodiment, repeatedly performing capture includes vtMinimum isolated set S ofmThe number of times (t) is 100, and when 100 times are captured, it can be considered that all the minimum isolated set S has been completely capturedm(t) of (d). In other embodiments, the capturing times can be selected according to actual conditions.
S3, according to a formula
Figure BDA0002112913710000061
Calculating vtFor viAnd vjInfluence of (2)
Figure BDA0002112913710000062
Where e is a natural base number, and calculating vtFor all with vtInfluence of the community core as a bridge point
Figure BDA0002112913710000063
Accumulate all
Figure BDA0002112913710000064
Get the communication importance Ct。
It should be noted that the connectivity importance CtThe method not only considers the number of the minimum isolation centralized nodes among community cores, but also considers the influence of the possible number of the minimum isolation centralized nodes on the connectivity performance. Communication importance CtThe method is applicable to large-scale networks and is an index for measuring the influence of the nodes from the whole network. Bridge nodeThe greater the communication importance of (A), the higher the importance of the bridge node in the bridge node. Conversely, the smaller the importance of bridge node connectivity, the lower the importance in the bridge node.
In addition, the communication importance degree C calculated in the present embodimenttThe whole community network graph is fully considered, and the accuracy of obtaining the social network key nodes is improved.
S4, repeatedly selecting vtAnd respectively calculate CtTaking out CtV corresponding to the maximum value oftNamely, the key nodes of the social network.
In the present embodiment, v is repeatedly selectedtThe number of times of (2) is 100, and C is finally obtainedtThe most valued is used as the key node of the social network. In other embodiments, the number of times of repeated selection can be selected according to actual conditions.
The method has the advantages that the community network is divided into a plurality of communities, the social network graph can be analyzed more comprehensively by acquiring the influence of different community cores, and the obtained social network key nodes are more accurate.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (4)

1. A method for obtaining a key node of a social network is applied to a social network graph and is characterized by comprising the following steps:
calculating the community importance index of each node in the social network graph, dividing the social network graph into a plurality of communities, and obtaining the node with the maximum community importance index in each community to obtain a community core;
acquiring the nodes communicated with the two community cores to obtain bridge nodes, and selecting any bridge node vtTaking any two community cores v which are mutually communicatediAnd vjObtaining viAnd vjCapturing the isolated set with the least number of nodes and making the isolated set include vtObtaining a minimum isolation set S (t), recording the node number alpha in S (t)tCounting all forms of S (t), recording the total number of said forms betat；
According to the formula
Figure FDA0002964050040000011
Calculating vtFor viAnd vjInfluence of (2)
Figure FDA0002964050040000012
Where e is a natural base number, and calculating vtFor all with vtInfluence of the community core as a bridge Point
Figure FDA0002964050040000013
Accumulate all
Figure FDA0002964050040000014
Get the communication importance Ct；
Repeat selection vtSeveral times and respectively calculate CtTaking out CtV corresponding to the maximum value oftNamely, social network key nodes;
wherein the obtaining viAnd vjThe step of isolating the set of cells of (1) comprises:
taking any node set, and judging v when the node set is deletediAnd vjWhether the connected state of (1) is disconnected;
if yes, the node set is viAnd vjThe isolated set of (2);
all forms of the statistics S (t), the total number of the forms beta is recordedtComprises the following steps:
performing a Capture includes vtMinimum isolated set S of1(t) adding S1(t) storing the data into a preset minimum isolation set database;
re-execution of the Capture includes vtMinimum isolated set S of2(t), judgment of S2(t) whether all the minimal set of isolation in the minimal set database are the same, if so, continuing to perform capture including vtMinimum isolated set S of3(t), otherwise S2(t) storing in said minimum insulation set database;
by analogy, repeatedly performing a number of captures includes vtMinimum isolated set S ofm(t), judgment of Sm(t) whether all the minimal set of isolation in the minimal set database are the same, if so, continuing to perform capture including vtMinimum isolated set S ofm+1(t), otherwise Sm(t) storing in said minimum insulation set database;
counting the minimum isolated set stored in the minimum isolated set database to obtain the number beta of all forms of S (t)t。
2. The method of claim 1, wherein the step of calculating the community importance index for each node in the social network graph comprises:
acquiring degree indexes of all nodes in the social network graph;
and repeatedly executing and deleting the node with the minimum degree index in the social network graph until all the nodes are deleted, and recording the times of repeatedly executing and deleting, namely the community importance index b.
3. The method of claim 1, wherein the step of dividing the social network graph into communities comprises:
substep 1, taking any node v0, and respectively obtaining a first-order neighbor node v1, a second-order neighbor node v2 and a third-order neighbor node v3 of v0, wherein v0, v1, v2 and v3 are sequentially connected;
substep 2, if v0 is larger than the community importance indexes of v1, v2 and v3, dividing v0, v1, v2 and v3 into a community;
and repeating the substep 1 and the substep 2, and dividing the whole social network graph into a plurality of communities.
4. A method of obtaining social networking key nodes according to claim 1, wherein in the step of obtaining nodes in communication with two of the community cores, the nodes in communication with the two community cores are located between the two community cores.
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